Chunbo Luo

Full Interference Cancellation for Two-Path Relay Cooperative Networks

This paper proposes the full interference cancellation (FIC) algorithm to cancel the interrelay interference (IRI) in the two-path cooperative system. Arising from simultaneous data transmission from the source and relay nodes, IRI may significantly decrease performance if it is not carefully handled. Compared with the existing partial interference cancellation scheme, the FIC approach is more robust yet is not as complex. Numerical results are also given to verify the proposed scheme.

UAV Position Estimation and Collision Avoidance Using the Extended Kalman Filter

Unmanned aerial vehicles (UAVs) play an invaluable role in information collection and data fusion. Because of their mobility and the complexity of deployed environments, constant position awareness and collision avoidance are essential. UAVs may encounter and/or cause danger if their Global Positioning System (GPS) signal is weak or unavailable. This paper tackles the problem of constant positioning and collision avoidance on UAVs in outdoor (wildness) search scenarios by using received signal strength (RSS) from the onboard communication module. Colored noise is found in the RSS, which invalidates the unbiased assumptions in least squares (LS) algorithms that are widely used in RSS-based position estimation. A colored noise model is thus proposed and applied in the extended Kalman filter (EKF) for distance estimation. Furthermore, the constantly changing path-loss factor during UAV flight can also affect the accuracy of estimation. To overcome this challenge, we present an adaptive algorithm to estimate the path-loss factor. Given the position and velocity information, if a collision is detected, we further employ an orthogonal rule to adapt the UAV predefined trajectory. Theoretical results prove that such an algorithm can provide effective modification to satisfy the required performance. Experiments have confirmed the advantages of the proposed algorithms.

A UAV-Cloud System for Disaster Sensing Applications

The application of small civilian unmanned aerial vehicles (UAVs) has attracted great interest for disaster sensing. However, the limited computational capability and low energy resource of UAVs present a significant challenge to real-time data processing, networking and policy making, which are of vital importance to many disaster related applications such as oil-spill detection and flooding. In order to address the challenges imposed by the sheer volume of captured data, particularly video data, the intermittent and limited network resources, and the limited resources on UAVs, a new cloud-supported UAV application framework has been proposed and a prototype system of such framework has been implemented in this paper. The framework integrates video acquisition, data scheduling, data offloading and processing, and network state measurement to deliver an efficient and scalable system. The prototype of the framework comprises of a client-side set of components hosted on the UAV which selectively offloads the captured data to a cloud-based server. The server provides real-time data processing and information feedback services to the incident control centre and client device/operator. Results of the prototype system are presented to demonstrate the feasibility of such framework.

Full Interference Cancellation for Two-Path Cooperative Communications

This paper proposes a full interference cancellation (FIC) approach for two-path cooperative communications. Unlike the single relay schemes, the two-path cooperative scheme involves two relay nodes, so that the source can continuously transmit data to the two relays alternatively and the full bandwidth efficiency with respect to the direct transmission can be retained. The two-path relay scheme may however suffer from inter-relay interference which is caused by the simultaneous transmission of the source and one of the relays at any time. In this paper, first the inter-relay interference is expressed as a single recursive term in the received signal, and then the FIC approach is proposed to fully remove the inter-relay interference. The FIC has not only better performance but also less complexity than existing approaches. Numerical examples are also given to verify the proposed approach.

Two-Path Succussive Relaying With Hybrid Demodulate and Forward

This paper proposes a novel demodulation-and-forward (DMF) scheme for the two-path succussive relay system. While the two-path relaying avoids the data rate loss that occurs in many one-relay cooperative systems, its performance is severely limited by interrelay interference. In this paper, we propose a hybrid DMF scheme for the two-path relay system so that the relays can switch between direct and differential demodulation modes according to channel conditions. The hybrid DMF scheme not only performs better than existing two-path approaches but is easy to achieve synchronization at the relays as well, which is particularly important as a relay receives signals from both the source and the other relay. The proposed hybrid DMF scheme provides an innovative way to implement the two-path relaying scheme.

Communication provision for a team of remotely searching UAVs: A mobile relay approach

In many applications it is desirable to deploy unmanned autonomous vehicles to remote real-world locations, at considerable distance from any fixed infrastructure, rendering direct communication impossible; for example in the Wilderness Search and Rescue (WiSaR) scenario. However, data transfer between such vehicles and other participants is required for control, safety, monitoring progress and sharing of acquired information. This paper proposes a method of using an Unmanned Aerial Vehicle (UAV) as a highly mobile relay, providing a delay tolerant link between a fixed base-station and a team of searching UAVs to meet this vital communication need. A communication model is described that permits a single relay to simultaneously collect data from multiple operational UAVs at pre-arranged meetings, both theoretical and experimental simulation results demonstrate the characteristics and effectiveness of this approach in a realistic cooperative sensing scenario.

Timely autonomous identification of UAV safe landing zones

For many applications such as environmental monitoring in the aftermath of a natural disaster and mountain search-and-rescue, swarms of autonomous Unmanned Aerial Vehicles (UAVs) have the potential to provide a highly versatile and often relatively inexpensive sensing platform. Their ability to operate as an ‘eye-in-the-sky’, processing and relaying real-time colour imagery and other sensor readings facilitate the removal of humans from situations which may be considered dull, dangerous or dirty. However, as with manned aircraft they are likely to encounter errors, the most serious of which may require the UAV to land as quickly and safely as possible. Within this paper we therefore present novel work on autonomously identifying Safe Landing Zones (SLZs) which can be utilised upon occurrence of a safety critical event. Safe Landing Zones are detected and subsequently assigned a safety score either solely using multichannel aerial imagery or, whenever practicable by fusing knowledge in the form of Ordnance Survey (OS) map data with such imagery. Given the real-time nature of the problem we subsequently model two SLZ detection options one of which utilises knowledge enabling the UAV to choose an optimal, viable solution. Results are presented based on colour aerial imagery captured during manned flight demonstrating practical potential in the methods discussed.

Deep learning in remote sensing scene classification: a data augmentation enhanced convolutional neural network framework

The recent emergence of deep learning for characterizing complex patterns in remote sensing imagery reveals its high potential to address some classic challenges in this domain, e.g. scene classification. Typical deep learning models require extremely large datasets with rich contents to train a multilayer structure in order to capture the essential features of scenes. Compared with the benchmark datasets used in popular deep learning frameworks, however, the volumes of available remote sensing datasets are particularly limited, which have restricted deep learning methods from achieving full performance gains. In order to address this fundamental problem, this article introduces a methodology to not only enhance the volume and completeness of training data for any remote sensing datasets, but also exploit the enhanced datasets to train a deep convolutional neural network that achieves state-of-the-art scene classification performance. Specifically, we propose to enhance any original dataset by applying three operations – flip, translation, and rotation to generate augmented data – and use the augmented dataset to train and obtain a more descriptive deep model. The proposed methodology is validated in three recently released remote sensing datasets, and confirmed as an effective technique that significantly contributes to potentially revolutionary changes in remote sensing scene classification, empowered by deep learning.

Interference cancellation in two-path successive relay system with network coding

This paper proposes a novel interference cancellation algorithm for the two-path succussive relay system using network coding. The two-path succussive relay scheme was proposed recently to achieve full date rate transmission with half-duplex relays. Due to the simultaneous data transmission at the relay and source nodes, the two-path relay suffers from the so-called inter-relay interference (IRI) which may significantly degrade the system performance. In this paper, we propose to use the network coding to remove the IRI such that the interference is first encoded with the network coding at the relay nodes and later removed at the destination. The network coding has low complexity and can well suppress the IRI. Numerical simulations show that the proposed algorithm has better performance than existing approaches.

Systematic infrared image quality improvement using deep learning based techniques

Infrared thermography (IRT, or thermal video) uses thermographic cameras to detect and record radiation in the longwavelength infrared range of the electromagnetic spectrum. It allows sensing environments beyond the visual perception limitations, and thus has been widely used in many civilian and military applications. Even though current thermal cameras are able to provide high resolution and bit-depth images, there are significant challenges to be addressed in specific applications such as poor contrast, low target signature resolution, etc. This paper addresses quality improvement in IRT images for object recognition. A systematic approach based on image bias correction and deep learning is proposed to increase target signature resolution and optimise the baseline quality of inputs for object recognition. Our main objective is to maximise the useful information on the object to be detected even when the number of pixels on target is adversely small. The experimental results show that our approach can significantly improve target resolution and thus helps making object recognition more efficient in automatic target detection/recognition systems (ATD/R).